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By Nashon Oriang, Graduate of the Presbyterian University of East Africa
Millions of workers logging into their offices, factories, and call centers, as well as their digital dashboards, on any given day have no idea that an unseen partner has entered the room; that silent partner is already there inside the room.
This partner is not human; it is Artificial Intelligence (AI).
In many common tasks, this means companies are now turning to AI systems for workload support. These systems help companies manage their workload, evaluate performance, make optimal decisions, optimize processes, and provide suggestions that were previously handled by human experts on the job, all done by professionals.
The surprising part, though, is that those developments occur with no reflection, input, or consent from the affected individuals.
AI is quietly but surely changing the way that people are working, whether that’s at hospitals, banks, marketing companies, or logistics companies. Automated scheduling software schedules labor based on productivity levels.
Predictive algorithms determine which customer complaints are prioritized.
Chatbots, which run artificial intelligence algorithms, take questions at a speed that nobody has ever experienced before.
And in some other cases, we have had that human deliberation that took weeks to machine-calculated, human, algorithmically processed. But efficiency, as many institutions have long since learned, also comes at a high cost.
AI, if it is used without consultation and input, can demotivate; it threatens trust; it disempowers employees at all levels to navigate systems they did not design or understand.
The problem of human-like, unregulated automation is one where this isn’t just a technological advancement, but something that touches the human race, with deep ethical questions. The dawn of Silent Automation.
Take the finance industry, for example.
Over the past four years, various banks have rolled out AI agents that can automate loan applications, check documents, and tag potential risks.
Management hailed the rollout, boasting of faster processing times, fewer errors, and consistent compliance. But people responsible for reviewing those applications often had no control over the system’s design or rollout.
They had little help in understanding how decisions would feed back to AI, so they learned new flows overnight.
The development of AI in business follows a similar formula and takes place in every industry.
For instance, hospitals start employing AI to analyze imaging data, forecast how a patient will recover, and assign them to priority treatments.
Nurses, radiology technicians, and administrative workers who come into daily contact with and learn from patients and know how complicated care is are often pushed to the fringe when it comes to making decisions or training.
AI is now directing messaging, scheduling posts, and separating customers from behavioral data in marketing, taking the duty of balancing algorithmic decisions with human judgment and creative teams.
In all these situations, the same pattern prevails: AI comes first, employees are alerted to it at a later date, and the machines are regarded as unbiased when they had, in fact, made human decisions programmed into code.
The High Costs of Efficiency
The implications that come with the adoption and application of AI without consultation aren’t all that significant, but they come with pernicious and widespread consequences.
Workers are working to acclimate to these new systems without feeling anxious, disoriented, and frustrated. This makes staff unlikely to complain about the content AI generates.
If the content doesn’t agree with what humans or the public know, work, or the conventional way of knowing, people have long ago stated that this may be the case.
So much of decision-making is being quietly moved out of humans’ control and into people’s hands by algorithms, cutting corners on decision makers’ autonomy in ways that ripple through teams.
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Algorithms do not even have their own objective character.
They rely on historical data that, say, has biases, gaps, and inequities.
When employees aren’t given the space to test or monitor or contextualize AI, these biases are invisible, entrenching legacy inequalities at work, in promotion decisions, or human-to-human conversations with customers.
One example, say, is a call center agent who gets scolded by a performance tracker for the use of culturally appropriate language but is deemed “inefficient” since the system has been trained on another dataset specifically for behavior detection.
This is ethical ground to tread on.
Automation, without consent, takes over the workforce so that humans are no longer agents but assistants.
It raises a potential legal conundrum as well: who is accountable if an AI errs? The machine? Management? Was the worker instructed to follow the structure and direction provided for that employee? Both of those questions largely lay unanswered by a lack of transparency and consultation.
Real-World Examples
There was an early introduction of AI in India regarding home loan processing, its promise, and challenges.
BASIC Home Loan, a financial services firm, recently rolled out a system to automate checking, sanctioning, and managing application documentation, using both AI agents and small language models as well as these systems.
Its system, as stated by its CEO, Atul Monga, was intended to remove human judgment and to promote neutrality and streamlined working processes.
The managerial perspective here is so clear and objective.
For workers, though, the story is more multifaceted.
Clerks and loan officers will have a new role controlling those systems, whose inner operations they can’t see, explaining outputs to customers they may not understand, and making individual exceptions without oversight.
They have the power of the AI; the workers have no voice. Consent was presumed but never requested firsthand.
Healthcare, after all, is yet another obvious example.
Hospitals are more and more resorting to AI for such things as imaging analysis, risk prediction, and patient triage.
But radiologists and nurses, who know those patients with complicated histories, are often asked to check, not guide care decisions, but confirm the output of AI systems.
AI systems trained to optimize for efficiency can unintentionally make the burden of fulfilling recommendations fall on staff, but also challenge them to act on such recommendations while overlooking their professional judgment, creating an ethical quandary between rapidity of access and quality.
This isn’t just the industries that are left in the dust. Today’s marketing companies are using AI to create copy, design ads, and campaigns for their businesses and improve customer engagement.
The human team is forced to modify outputs and make decisions often at times without knowledge of what the decisions are based on, while the human hands are forced to edit and decide whatever, or on any output of decisions not necessarily what is in their mind; without a clue to what was determined in such a process, their decision-making process gets transformed by such choices by such algorithms.
When these priorities get automated, creativity disappears, and workers are pushed away from the work that they produce.
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Why Consent Matters
Automation without consent isn’t merely a technology issue; it’s also a people issue.
Workers serve as experts in the area of human knowledge, which no single algorithm is capable of producing.
If they don’t feel welcome, they get robbed of beneficial information, increased opportunities for error, and the trust organizations have in their application, as they lose access to important ’real intelligence’, as well as trust in AI.
Consultation is a nice way to remember human expertise as an addendum, not an absolute replacement of AI.
If you have staff who know how such systems work, they can identify blind spots in use, help identify bias, and advise organizations on how their workers can best integrate workflows.
Accountability and morale must be kept up by training and openness with the training.
Consent in this case is not only a legal or moral duty, but a productive approach.
Systems operate more efficiently when those who operate them are credible allies to be employed and educated partners with intelligence.
The Ethical Imperative. Institutions have a choice. AI can be a force for efficiency, innovation, and enlightenment if we don’t forget that humans are also involved.
Without involving workers in the decision-making of design and deployment, the result can be workplaces where responsibility gets mixed up with algorithms, decisions are unnecessary, and personal accountability becomes collective.
It becomes much worse in high-stakes situations, where one consequence can reverberate throughout.
AI errors can be life-altering for those in the fields of healthcare, finance, and public services.
There will be chances for errors to go undetected, bias to develop, and no accountability to be maintained if employees are left out of the decisions that impact them.
It’s not simply a simple efficiency in the language of unassisted automation; automation without your consent has no neutrality at all because there is no exit from a scale. Steps Forward.
Consultation and transparency ought to carry weight with institutions seeking to be AI-responsible:
Early Participation
Include staff in the design process, not only at the outset.
Ask how processes operate, where they are hurting, and how AI can help do so, not help undermine them.
Education and Training
Enable your team to get a feel for the function of AI, what kind of data it is powered by, and what its output really looks like. Knowledge is power, consent is knowledge.
Feedback Loops
Provide employees with the space to identify errors, biases, and inefficiencies.
AI should be flexible, not inflexible, and employee feedback is important for optimization.
Accountability and Transparency
Define exactly the mechanisms for accountability. When the machine does make a mistake, the workforce in which it operates, including managers, should know whose fingers can intervene.
Integration in a Minute’s Work
You can’t do it all.
Use systems gradually to let the employees understand and provide feedback on an iterative basis before automation is fully implemented.
Those organizations that do not adhere to those benchmarks are bound to hurt more than they do to make happy workers.
They risk making inequities seem normalized and undermining morale and ultimately devaluing automation altogether.
AI is brilliant, but without consensus, it becomes a stoic dictator rather than a helpful assistant.
A Human-Centered Future
AI will stick around, and automation will only grow. But successful organizations are only possible out of awareness that the human component is also the essence of work.
Employees aren’t stumbling blocks to efficiency; they’re partners in making automation more intelligent, safer, and humane as well.
It’s not anymore whether AI will generate jobs, but how.
Institutions must ensure that they consult and inform employees and offer them power.
Not those automated acts, unwanted or not, will become a foregone conclusion.
If you are going to do it in a deliberate way and in a way that makes it right, yes, planned by input from process and human and processes as well as people, then AI should be the tool that adds and not the replacement of people.
The simple truth is this: efficiency without consent is not innovation, not progress.
If AI can amplify human judgment, AI should not silence; it can only magnify it.
Machines can do more work at a much quicker rate, so, at the end of the day, we are going to have to control decisions, ethics, and responsibility.
Only then will automation fulfill its role: helping to support rather than managing people, not providing for them in silence.
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